What is the difference between Covariance and Correlation?

Correlation and covariance are statistical terms that are used to determine the relationship between two random variables. Covariance shows the direction of the linear relationship between variables, while correlation measures the strength and direction of a linear relationship between two variables.

Correlation provides a certain amount of covariance on a standard scale. Correlation can be deduced from covariance; it is deduced by dividing the calculated covariance with standard deviation. Covariance value is within the range of negative infinity and positive infinity, while correlation is limited to values within the range of -1 and +1.

Scalability affects covariance, while correlation is not affected by the change is constant. Covariance has a definite unit because it is deduced by the multiplication of two unit numbers, while correlation is a unitless.

The subject of mathematics has many aspects to it, and one thing that a student will be required to learn in math is variables. A person will learn how to measure between two variables. This is where Covariance and Correlation come in. Covariance is related to the direction in which two variables have a relationship.

This helps learn the impact of one variable's change on another variable. Correlation is also in regard to the relationship between the different variables, but it also is used to measure the strengths between them. This focuses more on the relationships between the two variables.

If there is one similarity between the two terms, it is the fact that it is meant to measure the dependency of the two variables. When you say “covariance,” this stands for the direction of the relationship between two different variables.

When you say “correlation,” this means that you are trying to measure the strength and the direction of the relationship, particularly the linear relationship, of two different variables.

For example, in covariance, you are trying to learn how one variable can change depending on the changes that are also happening to the other variable. For correlation, you would like to know how much one variable is related to the other.